Mapping Teachers Need for Generative AI Integration in Chemistry Education: A Needs Assessment Study

Faathir Almur; Munawwarah Munawwarah

doi:10.55927/eajmr.v5i5.92

Authors

Faathir Almur Universitas Negeri Makassar
Munawwarah Munawwarah Universitas Negeri Makassar

DOI:

https://doi.org/10.55927/eajmr.v5i5.92

Keywords:

Generative AI, Chemistry Education, Needs Assessment, Teacher Training, Borich Model

Abstract

This study maps chemistry teachers' needs in integrating generative AI into instruction using the Borich Needs Assessment Model. 43 high school chemistry teachers from South and West Sulawesi, Indonesia, completed a self-reported questionnaire. Mean Weighted Discrepancy Scores (MWDS) revealed the highest needs in AI training provision, followed by AI utilization for chemistry content visualization and institutional infrastructure support. Lower needs were observed in basic digital literacy and AI ethics. Findings emphasize that effective AI integration requires sustained, context-specific professional development and systemic institutional support tailored to the unique demands of chemistry education.

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